Lead Data Scientist

About ProcDNA

ProcDNA is a global consulting firm. We fuse design thinking with cutting-edge tech to create game-changing Commercial Analytics and Technology solutions for our clients. We're a passionate team of 470+ across 9 offices, all growing and learning together since our launch during the pandemic. Here, you won't be stuck in a cubicle. Instead, you'll be out in the open water, shaping the future with brilliant minds. Ready to join our epic growth journey?
 

What are we looking for

We are looking for a Lead Data Scientist who can own data science engagements end to end, from shaping the problem with the client to delivering and operationalizing ML solutions. You will lead a team of data scientists, guide technical decisions, and help grow ProcDNA's data science practice through client relationships and new capabilities.

What you'll do:

• Work with clients to translate business questions into well-scoped data science problems, and define the approach, data requirements and success measures.
• Design and oversee ML solutions across the pharma commercial lifecycle, and choose methods that fit the data and the decision they support.
• Own client communication and relationships and provide thought leadership to support their business decisions.
• Quality-check the team's work and guide models to production through cross-team collaboration.
• Lead project delivery across timelines, scope and team allocation, often across parallel engagements.
• Mentor and manage a team of data scientists, and keep yourself and the team current with advancements in the field for collective growth.
• Contribute to business development and internal capability building through proposals, POCs and reusable offerings, and help the practice grow

Must have:

• 5.5+ years of hands-on experience in data science and ML, with at least 2 years leading projects or teams in a pharma consulting or client-facing setting.
• A Bachelor's or Master's degree in engineering, statistics, mathematics or a relevant quantitative field.
• Experience with life sciences data, such as claims (Komodo, IQVIA, Symphony), specialty pharmacy, CRM, DDD, Lab and other HCP, account or patient-level data.
• Strong depth in Statistics, supervised and unsupervised ML, Deep learning and GenAI concepts, along with experience in deploying models to production.
• Strong Python and SQL, with experience on distributed or cloud platforms such as Databricks, PySpark and AWS/Azure.
• A track record of owning client communication and explaining results to non-technical stakeholders.
• Experience in guiding and managing a team of data scientists.